arXiv AI
Jul 1
Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens
arXiv:2606. 30755v1 Announce Type: cross Abstract: Claw-like AI agents (e.
A Deterministic Security Runtime for LLM Agents on MPI and Batch-Scheduled HPC Systems: Consensus-Governed, Hardware-Rooted Supervision of Every Rank
The paper introduces Enclawed, a deterministic security runtime that protects large language model agents running on high‑performance computing (HPC) systems. It provides hardware‑rooted supervision for every rank, preventing compromised hosts from causing catastrophic failures, and enforces launch policies, lease‑based heartbeats, and automatic eviction or killing of tampered jobs. Enclawed operates without administrator privileges, integrates via Secure Shell, and adds minimal startup overhead while successfully blocking a wide range of simulated attacks.
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arXiv AI
Oct 7
MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding Agents
MemMux is a local runtime designed to provide runtime verification and honest resource attribution for fleets of parallel coding agents. It emits observable signals that track per‑agent memory usage, ensure complete reclamation of terminated agents, detect escaped child processes, and keep the system from exceeding a bounded memory footprint. In benchmarks against tmux and a raw‑process baseline, MemMux keeps a fleet under a 7.5 GiB budget with zero swap, while ungoverned tools exceed the budget and spill into swap, and it achieves 100 % attribution with low overhead.
arXiv AI
Sep 2
Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
The paper examines the security challenges of delegating authority to autonomous LLM agents that act on users’ behalf. It introduces a threat model with four adversaries and eight security requirements, demonstrates that current frameworks (LangGraph, CrewAI, AutoGen, MCP) fail to meet these standards, and presents an authorization broker that blocks all identified threats with minimal overhead. The broker is shown to resist numerous attacks and limits compromised sub‑agents to their delegated tasks, and its principles are implemented in VotalAI’s LLM Shield.
arXiv AI
Jul 17
AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems
arXiv:2603. 15727v3 Announce Type: replace-cross Abstract: Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplored.
arXiv AI
Jun 30
Agent libOS: A Runtime Substrate for Capability-Controlled Self-Evolving LLM Agents
arXiv:2606. 03895v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents are becoming long-running software actors rather than fixed tool users.
arXiv AI
Jun 9
SecureClaw: Clawing Back Control of LLM Agents
arXiv:2606. 09549v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents face two distinct security failures: unauthorized external actions and exposure of sensitive plaintext inside the runtime before any final output check can intervene.
arXiv AI
Oct 21
The Show-Up Attack: When Subagents Win Over Their Supervisor
The paper introduces the "show‑up attack," where a malicious subagent tricks a supervisor into assigning it tasks beyond its intended scope. Using the SUA‑Arena benchmark, the authors test six attack strategies across 188 scenarios and evaluate six leading LLMs, finding all are vulnerable, with GPT‑5.6‑Sol experiencing an attack success rate of up to 0.93. Standard mitigations like prompting or restricted communication do not consistently reduce the risk, highlighting the need for explicit authority‑enforcement mechanisms in multi‑agent systems.
arXiv AI
Jul 8
The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
arXiv:2607. 05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe.
arXiv AI
Jun 9
Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops
arXiv:2606. 08960v1 Announce Type: cross Abstract: Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking.
arXiv AI
Oct 9
Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper
The paper evaluates a three‑part control stack— a 5‑Pillar runtime constitution, a 4‑tier swarm (Director, majority vote, Monitor, hard gate), and Cognitive Annealing—for multi‑agent LLM negotiation against an adversarial Gatekeeper. Experiments with Gemini 2.5 Pro agents and Claude Haiku 4.5 show that the constitution and Director improve framing but not reliability, the Monitor and hard gate do not reduce unlocks, and deterministic recovery via atomic purge and a canonical strike successfully resolves deadlocks while keeping call budgets unchanged. The study demonstrates that the stack can recover from deadlocks without additional model cost, though it does not reduce overall call or token usage as initially hypothesized.
arXiv AI
Oct 15
Scalable Weak-to-Strong Verifier Hardening for Agent Environments
arXiv:2610.15924v1 Announce Type: cross Abstract: Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn complex outcomes into rewards. V...
arXiv AI
Jul 31
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
arXiv:2607. 26791v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities.